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Internet of Things Blogs

The Evolution of Digital Twins for Asset Operators

In Part I of this series on Digital Twins (DT) Andy crafted a great explanation of what a DT is, why it's need and the guiding principles on building DTs for Asset Operators. In Part II, I’ll address is the evolution of how we, as industrial asset operators will go through in adopting Digital Twins.

To refresh, a digital twin is a dynamic digital representation of the physical environment. We’re mostly familiar with DTs from the consumer world as the apps on our phones that manage your Nest Thermostats, the Philips Hue Light System’s color of the lights, the information about a Fitbit, or to summon a Tesla.

Unfortunately, for asset operators, these app-based consumer DTs don’t scale to the hundreds of thousands, even  millions, of measurement data streams required to operate modern industrial equipment and assets, whether it’s a discrete manufacturing line, oil refinery, or a large scale film production studio.

Two major trends, while creating a lot of marketing buzz, are pressuring asset operators and their supporting IT organization to act:

  1. Greater connectivity of our equipment with more sensing, typically referred to as “IIoT” or the Industrial Internet of Things
  2. Cheaper compute and storage allowing for more powerful analysis and operational improvement, a trend typically referred to as “Industry 4.0” or “Digital Transformation”

To connect those two trends you need digital twins. But which digital twin should asset operators pursue and how should they get started? We see DT’s evolving in stages, and the good news is that through past investments many companies already have ingredients in place to begin (and many have already started and not realized) their journey.

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Using Asset Data Models to Empower Your Industrial Organization

When I speak with CIOs and their staff, the topic of digital transformation always leads to a discussion around how time-series data is the starting point, but it’s difficult to work with and organize in a way that represents how equipment and assets exist in the physical world.

Industrial companies have begun to address the problem by adopting Asset Data Models, which represent the physical structures and relationships of industrial equipment and processes. Asset Data Models are crucial for equipment benchmarking, cross-site comparisons, and underpin every kind of analytics. 

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St Louis Arch

4 Steps to Data Readiness for Industrial Analytics

I recently attended a workshop in St. Louis—Big Data, Predictive Analytics and the Industrial Internet of Things—sponsored by our partner OSIsoft, Rockwell Automation and Anheuser-Busch (the makers of Budweiser beer for the uninitiated).  The event focused on how industrial companies can deploy new technologies like cloud, machine learning and mobile to turn raw data into analytical information that improve businesses outcomes.  

The presentations were informative, but mostly missing from the discussion was a deeper exploration of how poor data quality makes effective industrial analytics hard to achieve, especially at an enterprise scale.

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Why We're Here

In the Industrial Internet age, the physical world is converging with the digital world. This brings the unprecedented availability of data and data processing technologies to bear on industrial operations. Based on sensor data, for example, how is equipment performing? When and which equipment parts will need maintenance? This kind of insight is extremely useful and valuable.

Our mission is to transform data into actionable insight that helps you make better, fact-based decisions for greater worker safety, operational efficiency, and revenue.

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